Bibliographic record
Abstract
The second decade will be as exciting as the first Evidence based medicine seeks to empower clinicians so that they can develop independent views regarding medical claims and controversies. Although many helped to lay the foundations of evidence based medicine,1 Archie Cochrane's insistence that clinical disciplines summarise evidence concerning their practices, Alvan Feinstein's role in defining the principles of quantitative clinical reasoning, and David Sackett's innovation in teaching critical appraisal all proved seminal. The term evidence based medicine,2 and the first comprehensive description of its tenets, appeared little more than a decade ago. In its original formulation, this discipline reduced the emphasis on unsystematic clinical experience and pathophysiological rationale, and promoted the examination of evidence from clinical research. Evidence based medicine therefore required new skills including efficient literature searching and the application of formal rules of evidence in evaluating the clinical literature. Important developments in evidence based medicine over the subsequent decade included the increasing popularity of structured abstracts3 and secondary journals summarising …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".